Bisection approach for pixel labelling problem

作者:

Highlights:

摘要

This paper formulates pixel labelling as a series of two-category classification. Unlike existing techniques, which assign a determinate label to each pixel, we assign a label set to each pixel and shrink the label set step by step. Determinate labelling is achieved within log2n (n is size of label set) steps. In each step, we bisect the label set into two subsets and discard the one with higher cost of assigning it to the pixel. Simultaneous labelling of an image is carried out by minimizing an energy function that can be minimized via graph cut algorithm. Based on the bisection approach, we propose a bitwise algorithm for pixel labelling, which set one bit of each pixel's label in each step. We apply the proposed algorithm to stereo matching and image restoration. Experimental results demonstrate that both good performance and high efficiency are achieved.

论文关键词:Pixel labelling,Markov random fields,Graph cut,Stereo

论文评审过程:Received 3 December 2007, Revised 11 August 2009, Accepted 1 November 2009, Available online 10 November 2009.

论文官网地址:https://doi.org/10.1016/j.patcog.2009.11.002